Matching the bouy data to the other data collected

hilo <- hbb_wku_h_xts
hilo <- data.frame(date=index(hilo), coredata(hilo))
hilo <- hilo[529:54816,]
hilo
# which(hilo$date=="2010-10-23 00:00:00") = index 529 

# which(hilo$date=="2016-12-31 23:00:00") = index 54816
length(hilo[,1]) # we are left with 54288 lines of data

54816 - length(hilo[,1]) # we lost 528 values 

Changing column names

# removing columns that we are not using 
hilo$date.2 <- NULL # another date column
hilo$date.1 <- NULL # another date column
hilo$BGARFU <- NULL # ?
hilo$cfs <- NULL
hilo$DOmgL <- NULL # dissolved oxygen
#hilo$Doper <- NULL # dissolved oxygen
hilo$PAR1 <- NULL # ?
hilo$pH <- NULL # pH
hilo$NTU <- NULL # a different measurement for turbitity
hilo$DOper10 <- NULL # dissolved oxygen

# colnames(hilo) <- c("Date", "cfs", "RiverFlow-cumec", "LogRiverFlow-cumec", "Chlorophyll-RFU", "Salinity-PPT", "Temp-C", "chlorophyll-calibrator", "Turbidity-NTU")
# does not work ???

hilo

====================================================

FULL DATA SET 2012-2016

Descriptives: PLots

River Flow FULL DATA SET

length(hilo$logcms[which(is.na(hilo$logcms)==TRUE)]) # 12 NAs 
which(is.na(hilo$logcms)==TRUE)

RiverFlow <- ggplot(hilo,  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))

CHL FULL DATA SET

length(hilo$ChlRFU[which(is.na(hilo$ChlRFU)==TRUE)]) # 20464 NAs

which(as.numeric(hilo$ChlRFU)==max(as.numeric(na.omit(hilo$ChlRFU)))) # 15.3 max 
# CHL tells us where in the data set this happened  
hilo[38974,]

CHL <- ggplot(hilo,  aes(x = date, y = as.numeric(ChlRFU))) + 
  geom_line()

print(CHL + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll  - relative fluorescence units (RFU)"))

Turbity FULL DATA SET

length(hilo$Corr.NTU[which(is.na(hilo$Corr.NTU)==TRUE)]) #15012 NAs

which(as.numeric(hilo$Corr.NTU)==max(as.numeric(na.omit(hilo$Corr.NTU)))) # 88.4
# tells us where in the data set this happened
hilo[33243,]

TURB <- ggplot(hilo,aes(x = date, y = as.numeric(Corr.NTU))) + 
  geom_line()

print(TURB + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))

Salinity FULL DATA SET

length(hilo$saltppt[which(is.na(hilo$saltppt)==TRUE)]) #11330 NAs

SALT <- ggplot(hilo,  aes(x = date, y = as.numeric(saltppt))) + 
  geom_line()

print(SALT + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))

======================================================== # Histograms FULL DATA SET ## River Flow Histogram

hist(as.numeric(hilo$logcms), main = "Histogram of Log River Flow", xlab = "Log River Flow")

# this looks okay

CHL Histogram

# VERY skewed
hist(as.numeric(hilo$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll  - relative fluorescence units (RFU)")

# this looks better
hist(log(as.numeric(hilo$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
# not sure what happens to units when taking the log 

Turbity Histogram

# VERY skewed
hist(as.numeric(hilo$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")

# this looks better
hist(log(as.numeric(hilo$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
# not sure what happens to units when taking the log 

Salinity Histogram

# skewed
hist(as.numeric(hilo$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")

# this is worst!
hist(log(as.numeric(hilo$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log 

========================================================= # NEW DATA SET-Modified Data 2013-2015

# start date: 2013-01-01 00:00:00
# end date: 2015-12-31 23:00:00
hilomodified <- hilo[19225:45504,]
length(hilo[,1])-length(hilomodified[,1])
# lost 28008 entries of data 

length(hilomodified[,1])-528 # we are left with 25752 entries of data 


head(hilomodified)
tail(hilomodified)

Descriptives on all variables MODIFIED DATA SET: Using Favstats

River Flow Favstats

library(mosaic)
favstats(hilomodified$cms)

CHL Favstats

favstats(hilomodified$ChlRFU)

Turbitity Favstats

favstats(hilomodified$Corr.NTU)

Salinity Favstats

favstats(hilomodified$saltppt)
favstats(hilomodified$TempC)
favstats(hilomodified$Doper)

===================================================== # MODIFIED DATA SET 2013-2015 # Descriptives: Plots

River Flow MODIFIED

length(hilomodified$logcms[which(is.na(hilomodified$logcms)==TRUE)]) # No NAs, YAY!
which(is.na(hilomodified$logcms)==TRUE)

RiverFlowMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlowMod + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))

CHL MODIFIED

sum(is.na(hilomodified$ChlRFU)==TRUE) # 1884 NAs
#which(is.na(hilomodified$ChlRFU)==TRUE)
# max(as.numeric(na.omit(hilo$ChlRFU))) # 15.3
which(as.numeric(hilomodified$ChlRFU)==15.3)
hilo[38974,]

CHLMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(ChlRFU))) + 
  geom_line()

print(CHLMod + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll  - relative fluorescence units (RFU)"))

Turbitity MODIFIED

length(hilomodified$Corr.NTU[which(is.na(hilomodified$Corr.NTU)==TRUE)]) # 2704 NAs
# max(as.numeric(na.omit(hilo$Corr.NTU))) # 88.4
which(as.numeric(hilo$Corr.NTU)==88.4)
hilo[33243,]

TURBMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(Corr.NTU))) + 
  geom_line()

print(TURBMod + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))

Salinity MODIFIED

length(hilomodified$saltppt[which(is.na(hilomodified$saltppt)==TRUE)]) # 2267 NAs

SALTMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(saltppt))) + 
  geom_line()

print(SALTMod + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))

Tempurature MODIFIED

length(hilomodified$TempC[which(is.na(hilomodified$TempC)==TRUE)]) # 2267 NAs

TempMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(TempC))) + 
  geom_line()

print(TempMod + ggtitle("Temperature")+labs(x="Time", y = "Temperature - Celsius"))

Dissolved Oxygen MODIFIED

length(hilomodified$Doper[which(is.na(hilomodified$Doper)==TRUE)]) # 2267 NAs

TempMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(Doper))) + 
  geom_line()

print(TempMod + ggtitle("Dissolved Oxygen")+labs(x="Time", y = "Dissolved Oxygen in percent of saturation"))

=================================================== # Histograms MODIFIED

River Flow MODIFIED

hist(as.numeric(hilomodified$cms), main = "Histogram of Log River Flow", xlab = "Log River Flow", breaks =90, xlim = c(0,100))

# this looks okay

CHL MODIFIED

# VERY skewed
hist(as.numeric(hilomodified$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll  - relative fluorescence units (RFU)")

# this looks better
hist(log(as.numeric(hilomodified$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
# not sure what happens to units when taking the log 

Turbitity MODIFIED

# VERY skewed
hist(as.numeric(hilomodified$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")

# this looks better
hist(log(as.numeric(hilomodified$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
# not sure what happens to units when taking the log 

Salinity MODIFIED

# skewed
hist(as.numeric(hilomodified$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")

# this is worst!
hist(log(as.numeric(hilomodified$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log 

=============================================================

Plot with ALL Var 2013-2015

It is hard to see what is going on

AllYears <- ggplot(hilomodified,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line()+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(AllYears + ggtitle("Hilo Bay")+labs(x="Time", y = "River Flow - cubic meters per second"))

============================================================== # Descriptives by Storm We are picking one storm from each year. We can indicate a storm has occurred by the extreme events in the river flow data. We will not use the log (which is logbase10) in order to see the extreme events When salinity is below 35 this also indicates a storm has occurred.

We will break the data set by year to find the most extreme event for each year.

hilo2013 <- hilomodified[1:8760,]

hilo2014 <- hilomodified[8761:17520,]

hilo2015 <- hilomodified[17521:length(hilomodified[,1]),]

2013 Data & Plot

# max(as.numeric(hilo2013$logcms)) # this is log base 10 
# This is NOT the natural log
max(as.numeric(hilo2013$cms)) # 207.186 
which(as.numeric(hilo2013$cms) == max(as.numeric(hilo2013$cms)))

hilo2013[3550,]
# all values for this storm are NA

plot2013 <- ggplot(hilo2013,  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(plot2013 + ggtitle("2013")+labs(x="Time", y = "River Flow - cubic meters per second"))

Split 2013 into 6 months to get a better visual

# R2013.1 <- ggplot(hilo2013[1:(length(hilo2013[,1])/2),],  aes(x = date, y = as.numeric(cms))) + 
#   geom_line(color="black")+
#   geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
#   geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
#   geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.1 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.1+ylim(0,40)
# 
# 
# R2013.2 <- ggplot(hilo2013[(length(hilo2013[,1])/2):length(hilo2013[,1]),],  aes(x = date, y = as.numeric(cms))) + 
#   geom_line(color="black")+
#   geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
#   geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
#   geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.2 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.2+ylim(0,40)

2014 Data & Plot

# Max river flow in the overall data set
max(as.numeric(hilo2014$cms))
max(as.numeric(hilomodified$cms))

which(as.numeric(hilo2014$cms) == max(as.numeric(hilo2014$cms)))

hilo2014[5263,]

R2014 <- ggplot(hilo2014,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(R2014 + ggtitle("2014")+labs(x="Time", y = "River Flow - cubic meters per second"))

2015 Data & Plot

max(as.numeric(hilo2015$cms))

which(as.numeric(hilo2015$cms) == max(as.numeric(hilo2015$cms)))

hilo2015[6475,]

R2015 <- ggplot(hilo2015,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(R2015 + ggtitle("2015")+labs(x="Time", y = "River Flow - cubic meters per second"))

# Separating the Data by Storm Events

Separating the Data by Storm

outcomes <- as.numeric(hilomodified$saltppt)<25 
index.lt25 <- which(outcomes==TRUE)

poss.storms <- hilomodified[index.lt25,]
poss.storms
outcomes.35 <- as.numeric(hilomodified$saltppt)<35 
index.lt35 <- which(outcomes==TRUE)
index.lt25
poss.storms35 <- hilomodified[index.lt25,]
poss.storms35

hilomodified$date[16]
storm.1.7.13 <- ggplot(hilomodified[145:168,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))

Trying to make the Rainfall plot easier to read

RiverFlow1 <- ggplot(hilomodified[1:100,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))

=======

getwd()
# write.csv(hilomodified,file="HiloBayNEW13to15.csv", row.names = FALSE)

NEW Change in River Flow Column

pos.neg <- as.numeric(hilomodified$cms) - 10

start <- vector()
end <- vector()

for(i in 1:length(pos.neg)){
  if(isTRUE(pos.neg[i] < 0 && pos.neg[i+1] > 0)){
    start[i] <- i
  }else if(isTRUE(pos.neg[i] > 0 && pos.neg[i+1] < 0)){
    end[i] <- i
    }
}
start
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length(start)
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 [ reached getOption("max.print") -- omitted 25214 entries ]
end<-end[!is.na(end)]
length(end)
[1] 72
for(i in 1:length(start)){
  storm <- ggplot(hilomodified[(start[i]-24):(end[i]+24),],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="green") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue") +
  geom_line(aes(y = as.numeric(saltppt)),color="red") +
  geom_line(aes(y = as.numeric(TempC)),color="purple") +
  geom_line(aes(y = as.numeric(Doper)),color="orange")

  print(storm + ggtitle("Storms")+labs(x="Time"))
}

ChangeRF <- function(x = vector()){
change <- c(0)
  for(i in 1:(length(x)-1)){
    change[i+1] <- x[i]-x[i+1]
  }
  return(change)
}
change.vector <- c(ChangeRF(as.numeric(hilomodified$cms)))
change.vector
length(ChangeRF(as.numeric(hilomodified$cms)))
length(hilomodified$cms) # same length

hilomodified$changeCMS <- change.vector

head(hilomodified)
# 10-11 negative start of storm
# 11-10 positive end of storm

positive.change <- vector()
negative.change <- vector()
index <- vector()


for(i in 1:length(hilomodified$cms)){
  if (hilomodified$changeCMS[i] < 0 ){
    negative.change[i] <- i
  }else{
    if(hilomodified$changeCMS[i] > 0){
      postive.change[i] <- i
    }
  }
  if(hilomodified$cms[i] >= 10 ){
    index[i] <- i
  }
}
index
positive.change
negative.change
length(which(index > 0)) 

storms <- sort(c(positive.change,negative.change), decreasing = FALSE)
storms
hilostorms <- hilomodified[storms,]
hilostorms
storm <- ggplot(hilostorms[50:250,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))

vector <- vector()
for(i in 1:length(storms)){
  if(isTRUE(abs(storms[i]-storms[i+1]) > 10)){
    stop <- storms[i]
  }else{
    stop <- 0
  }
  if(stop > 0){
    vector[i] <- stop
  }
}
vector[which(is.na(vector)==FALSE)]

storm.1.7.13 <- ggplot(hilomodified[1:80,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
startaxis <- vector()
endaxis <- vector()
i <- 1
while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] < 0)){
    startaxis[i] <- RiverFlow[i]
  }
  i <- i+1
}
begin<-which(is.na(startaxis) == FALSE)
begin

while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] > 0)){
    endaxis[i] <- RiverFlow[i]
  }
  i <- i+1
}
end <- which(is.na(endaxis)==FALSE)


storm.1.7.13 <- ggplot(hilomodified[begin,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] < 0)){
    startaxis[i] <- RiverFlow[i]
    i <- i+1
  }else{
    if(isTRUE(change.vector[i] > 0)){
      endaxis[i] <- RiverFlow[i]
    }
  }
}
---
title: "Data Cleaning & Descriptives"
author: "Brianna Cirillo & Odalys Barrientos"
output: html_notebook
---
# Matching the bouy data to the other data collected 
```{r}
hilo <- hbb_wku_h_xts
hilo <- data.frame(date=index(hilo), coredata(hilo))
hilo <- hilo[529:54816,]
hilo
# which(hilo$date=="2010-10-23 00:00:00") = index 529 

# which(hilo$date=="2016-12-31 23:00:00") = index 54816
```
```{r}
length(hilo[,1]) # we are left with 54288 lines of data

54816 - length(hilo[,1]) # we lost 528 values 
```

# Changing column names 
```{r}
# removing columns that we are not using 
hilo$date.2 <- NULL # another date column
hilo$date.1 <- NULL # another date column
hilo$BGARFU <- NULL # ?
hilo$cfs <- NULL
hilo$DOmgL <- NULL # dissolved oxygen
#hilo$Doper <- NULL # dissolved oxygen
hilo$PAR1 <- NULL # ?
hilo$pH <- NULL # pH
hilo$NTU <- NULL # a different measurement for turbitity
hilo$DOper10 <- NULL # dissolved oxygen

# colnames(hilo) <- c("Date", "cfs", "RiverFlow-cumec", "LogRiverFlow-cumec", "Chlorophyll-RFU", "Salinity-PPT", "Temp-C", "chlorophyll-calibrator", "Turbidity-NTU")
# does not work ???

hilo
```

====================================================

# FULL DATA SET 2012-2016
# Descriptives: PLots

## River Flow FULL DATA SET
```{r}
length(hilo$logcms[which(is.na(hilo$logcms)==TRUE)]) # 12 NAs 
which(is.na(hilo$logcms)==TRUE)

RiverFlow <- ggplot(hilo,  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
```
## CHL FULL DATA SET
```{r}
length(hilo$ChlRFU[which(is.na(hilo$ChlRFU)==TRUE)]) # 20464 NAs

which(as.numeric(hilo$ChlRFU)==max(as.numeric(na.omit(hilo$ChlRFU)))) # 15.3 max 
# CHL tells us where in the data set this happened  
hilo[38974,]

CHL <- ggplot(hilo,  aes(x = date, y = as.numeric(ChlRFU))) + 
  geom_line()

print(CHL + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll  - relative fluorescence units (RFU)"))
```
## Turbity FULL DATA SET
```{r}
length(hilo$Corr.NTU[which(is.na(hilo$Corr.NTU)==TRUE)]) #15012 NAs

which(as.numeric(hilo$Corr.NTU)==max(as.numeric(na.omit(hilo$Corr.NTU)))) # 88.4
# tells us where in the data set this happened
hilo[33243,]

TURB <- ggplot(hilo,aes(x = date, y = as.numeric(Corr.NTU))) + 
  geom_line()

print(TURB + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))
```
## Salinity FULL DATA SET
```{r}
length(hilo$saltppt[which(is.na(hilo$saltppt)==TRUE)]) #11330 NAs

SALT <- ggplot(hilo,  aes(x = date, y = as.numeric(saltppt))) + 
  geom_line()

print(SALT + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))
```

========================================================
# Histograms FULL DATA SET
## River Flow Histogram
```{r}
hist(as.numeric(hilo$logcms), main = "Histogram of Log River Flow", xlab = "Log River Flow")

# this looks okay
```
## CHL Histogram
```{r}
# VERY skewed
hist(as.numeric(hilo$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll  - relative fluorescence units (RFU)")

# this looks better
hist(log(as.numeric(hilo$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
# not sure what happens to units when taking the log 
```
## Turbity Histogram
```{r}
# VERY skewed
hist(as.numeric(hilo$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")

# this looks better
hist(log(as.numeric(hilo$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
# not sure what happens to units when taking the log 
```
## Salinity Histogram
```{r}
# skewed
hist(as.numeric(hilo$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")

# this is worst!
hist(log(as.numeric(hilo$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log 
```

=========================================================
# NEW DATA SET-Modified Data 2013-2015
```{r}
# start date: 2013-01-01 00:00:00
# end date: 2015-12-31 23:00:00
hilomodified <- hilo[19225:45504,]
length(hilo[,1])-length(hilomodified[,1])
# lost 28008 entries of data 

length(hilomodified[,1])-528 # we are left with 25752 entries of data 


head(hilomodified)
tail(hilomodified)

```

# Descriptives on all variables MODIFIED DATA SET: Using Favstats
## River Flow Favstats
```{r}
library(mosaic)
favstats(hilomodified$cms)
```
## CHL Favstats
```{r}
favstats(hilomodified$ChlRFU)
```
## Turbitity Favstats
```{r}
favstats(hilomodified$Corr.NTU)
```
## Salinity Favstats
```{r}
favstats(hilomodified$saltppt)
favstats(hilomodified$TempC)
favstats(hilomodified$Doper)
```

=====================================================
# MODIFIED DATA SET 2013-2015
# Descriptives: Plots

## River Flow MODIFIED
```{r}
length(hilomodified$logcms[which(is.na(hilomodified$logcms)==TRUE)]) # No NAs, YAY!
which(is.na(hilomodified$logcms)==TRUE)

RiverFlowMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlowMod + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
```
## CHL MODIFIED
```{r}
sum(is.na(hilomodified$ChlRFU)==TRUE) # 1884 NAs
#which(is.na(hilomodified$ChlRFU)==TRUE)
# max(as.numeric(na.omit(hilo$ChlRFU))) # 15.3
which(as.numeric(hilomodified$ChlRFU)==15.3)
hilo[38974,]

CHLMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(ChlRFU))) + 
  geom_line()

print(CHLMod + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll  - relative fluorescence units (RFU)"))
```


# Turbitity MODIFIED
```{r}
length(hilomodified$Corr.NTU[which(is.na(hilomodified$Corr.NTU)==TRUE)]) # 2704 NAs
# max(as.numeric(na.omit(hilo$Corr.NTU))) # 88.4
which(as.numeric(hilo$Corr.NTU)==88.4)
hilo[33243,]

TURBMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(Corr.NTU))) + 
  geom_line()

print(TURBMod + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))
```

# Salinity MODIFIED
```{r}
length(hilomodified$saltppt[which(is.na(hilomodified$saltppt)==TRUE)]) # 2267 NAs

SALTMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(saltppt))) + 
  geom_line()

print(SALTMod + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))
```
# Tempurature MODIFIED
```{r}
length(hilomodified$TempC[which(is.na(hilomodified$TempC)==TRUE)]) # 2267 NAs

TempMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(TempC))) + 
  geom_line()

print(TempMod + ggtitle("Temperature")+labs(x="Time", y = "Temperature - Celsius"))
```
# Dissolved Oxygen MODIFIED
```{r}
length(hilomodified$Doper[which(is.na(hilomodified$Doper)==TRUE)]) # 2267 NAs

TempMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(Doper))) + 
  geom_line()

print(TempMod + ggtitle("Dissolved Oxygen")+labs(x="Time", y = "Dissolved Oxygen in percent of saturation"))
```
===================================================
# Histograms MODIFIED 

## River Flow MODIFIED
```{r}
hist(as.numeric(hilomodified$cms), main = "Histogram of Log River Flow", xlab = "Log River Flow", breaks =90, xlim = c(0,100))

# this looks okay
```
## CHL MODIFIED
```{r}
# VERY skewed
hist(as.numeric(hilomodified$ChlRFU), main = "Histogram of Chlorophyll", xlab = "Chlorophyll  - relative fluorescence units (RFU)")

# this looks better
hist(log(as.numeric(hilomodified$ChlRFU)), main = "Histogram of Log Chlorophyll", xlab = "Chlorophyll")
# not sure what happens to units when taking the log 
```
## Turbitity MODIFIED
```{r}
# VERY skewed
hist(as.numeric(hilomodified$Corr.NTU), main = "Histogram of Turbidity", xlab = "Turbidity - Nephelometric Turbidity Units (NTU)")

# this looks better
hist(log(as.numeric(hilomodified$Corr.NTU)), main = "Histogram of Log Turbidity", xlab = "Turbidity")
# not sure what happens to units when taking the log 
```
## Salinity MODIFIED
```{r}
# skewed
hist(as.numeric(hilomodified$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")

# this is worst!
hist(log(as.numeric(hilomodified$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log 
```
=============================================================

# Plot with ALL Var 2013-2015
It is hard to see what is going on 
```{r}
AllYears <- ggplot(hilomodified,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line()+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(AllYears + ggtitle("Hilo Bay")+labs(x="Time", y = "River Flow - cubic meters per second"))

```


==============================================================
# Descriptives by Storm
We are picking one storm from each year. We can indicate a storm has occurred by the extreme events in the river flow data. 
We will not use the log (which is logbase10) in order to see the extreme events
When salinity is below 35 this also indicates a storm has occurred. 

We will break the data set by year to find the most extreme event for each year. 
```{r}
hilo2013 <- hilomodified[1:8760,]

hilo2014 <- hilomodified[8761:17520,]

hilo2015 <- hilomodified[17521:length(hilomodified[,1]),]
```

# 2013 Data & Plot
```{r}
# max(as.numeric(hilo2013$logcms)) # this is log base 10 
# This is NOT the natural log
max(as.numeric(hilo2013$cms)) # 207.186 
which(as.numeric(hilo2013$cms) == max(as.numeric(hilo2013$cms)))

hilo2013[3550,]
# all values for this storm are NA

plot2013 <- ggplot(hilo2013,  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(plot2013 + ggtitle("2013")+labs(x="Time", y = "River Flow - cubic meters per second"))
```

#### Split 2013 into 6 months to get a better visual
```{r}
# R2013.1 <- ggplot(hilo2013[1:(length(hilo2013[,1])/2),],  aes(x = date, y = as.numeric(cms))) + 
#   geom_line(color="black")+
#   geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
#   geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
#   geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.1 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.1+ylim(0,40)
# 
# 
# R2013.2 <- ggplot(hilo2013[(length(hilo2013[,1])/2):length(hilo2013[,1]),],  aes(x = date, y = as.numeric(cms))) + 
#   geom_line(color="black")+
#   geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
#   geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
#   geom_line(aes(y=as.numeric(ChlRFU)),color="blue")
# print(R2013.2 + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
# R2013.2+ylim(0,40)
```

# 2014 Data & Plot
```{r}
# Max river flow in the overall data set
max(as.numeric(hilo2014$cms))
max(as.numeric(hilomodified$cms))

which(as.numeric(hilo2014$cms) == max(as.numeric(hilo2014$cms)))

hilo2014[5263,]

R2014 <- ggplot(hilo2014,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(R2014 + ggtitle("2014")+labs(x="Time", y = "River Flow - cubic meters per second"))
```


# 2015 Data & Plot
```{r}
max(as.numeric(hilo2015$cms))

which(as.numeric(hilo2015$cms) == max(as.numeric(hilo2015$cms)))

hilo2015[6475,]

R2015 <- ggplot(hilo2015,  aes(x = date, y = as.numeric(logcms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "darkred") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="darkgreen") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue")

print(R2015 + ggtitle("2015")+labs(x="Time", y = "River Flow - cubic meters per second"))
```


# Separating the Data by Storm Events
=======
# Separating the Data by Storm

```{r}
outcomes <- as.numeric(hilomodified$saltppt)<25 
index.lt25 <- which(outcomes==TRUE)

poss.storms <- hilomodified[index.lt25,]
poss.storms
```


```{r}
outcomes.35 <- as.numeric(hilomodified$saltppt)<35 
index.lt35 <- which(outcomes==TRUE)
index.lt25
poss.storms35 <- hilomodified[index.lt25,]
poss.storms35

hilomodified$date[16]
```

```{r}
storm.1.7.13 <- ggplot(hilomodified[145:168,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
```

# Trying to make the Rainfall plot easier to read

```{r}
RiverFlow1 <- ggplot(hilomodified[1:100,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))
```
=======
```{r}
getwd()
# write.csv(hilomodified,file="HiloBayNEW13to15.csv", row.names = FALSE)

```

# NEW Change in River Flow Column 

```{r}
pos.neg <- as.numeric(hilomodified$cms) - 10

start <- vector()
end <- vector()

for(i in 1:length(pos.neg)){
  if(isTRUE(pos.neg[i] < 0 && pos.neg[i+1] > 0)){
    start[i] <- i
  }else if(isTRUE(pos.neg[i] > 0 && pos.neg[i+1] < 0)){
    end[i] <- i
    }
}
start
start<-start[!is.na(start)]
length(start)
end
end<-end[!is.na(end)]
length(end)
```


```{r}
for(i in 1:length(start)){
  storm <- ggplot(hilomodified[(start[i]-24):(end[i]+24),],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="green") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="blue") +
  geom_line(aes(y = as.numeric(saltppt)),color="red") +
  geom_line(aes(y = as.numeric(TempC)),color="purple") +
  geom_line(aes(y = as.numeric(Doper)),color="orange")

  print(storm + ggtitle("Storms")+labs(x="Time"))
}

```

```{r}
ChangeRF <- function(x = vector()){
change <- c(0)
  for(i in 1:(length(x)-1)){
    change[i+1] <- x[i]-x[i+1]
  }
  return(change)
}
change.vector <- c(ChangeRF(as.numeric(hilomodified$cms)))
change.vector
length(ChangeRF(as.numeric(hilomodified$cms)))
length(hilomodified$cms) # same length

hilomodified$changeCMS <- change.vector

head(hilomodified)
```

```{r}
# 10-11 negative start of storm
# 11-10 positive end of storm

positive.change <- vector()
negative.change <- vector()
index <- vector()


for(i in 1:length(hilomodified$cms)){
  if (hilomodified$changeCMS[i] < 0 ){
    negative.change[i] <- i
  }else{
    if(hilomodified$changeCMS[i] > 0){
      postive.change[i] <- i
    }
  }
  if(hilomodified$cms[i] >= 10 ){
    index[i] <- i
  }
}
index
positive.change
negative.change
length(which(index > 0)) 

storms <- sort(c(positive.change,negative.change), decreasing = FALSE)
storms
hilostorms <- hilomodified[storms,]
hilostorms
```

```{r}
storm <- ggplot(hilostorms[50:250,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))
```

```{r}

vector <- vector()
for(i in 1:length(storms)){
  if(isTRUE(abs(storms[i]-storms[i+1]) > 10)){
    stop <- storms[i]
  }else{
    stop <- 0
  }
  if(stop > 0){
    vector[i] <- stop
  }
}
vector[which(is.na(vector)==FALSE)]

storm.1.7.13 <- ggplot(hilomodified[1:80,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(saltppt)), color = "lightsteelblue1") +
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))
```

```{r}
startaxis <- vector()
endaxis <- vector()
i <- 1
while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] < 0)){
    startaxis[i] <- RiverFlow[i]
  }
  i <- i+1
}
begin<-which(is.na(startaxis) == FALSE)
begin

while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] > 0)){
    endaxis[i] <- RiverFlow[i]
  }
  i <- i+1
}
end <- which(is.na(endaxis)==FALSE)


storm.1.7.13 <- ggplot(hilomodified[begin,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))

```

```{r}
while (i <= length(change.vector)) {
  if(isTRUE(change.vector[i] < 0)){
    startaxis[i] <- RiverFlow[i]
    i <- i+1
  }else{
    if(isTRUE(change.vector[i] > 0)){
      endaxis[i] <- RiverFlow[i]
    }
  }
}
```

